/// <inheritdoc/>
protected override void UpdateFactors(int u, int i, int j, bool update_u, bool update_i, bool update_j)
{
double x_uij = Predict(u, i) - Predict(u, j);
double one_over_one_plus_ex = 1 / (1 + Math.Exp(x_uij));
double learn_rate_u = learn_rate;
double learn_rate_i = learn_rate;
double learn_rate_j = learn_rate;
// TODO do this via sampling, not learn rate modification
if (!TestUsers.Contains(u))
{
learn_rate_u *= NonTestModifier;
}
if (!TestItems.Contains(i))
{
learn_rate_i *= NonTestModifier;
}
if (!TestItems.Contains(j))
{
learn_rate_j *= NonTestModifier;
}
// adjust bias terms
if (update_i)
{
double bias_update = one_over_one_plus_ex - BiasReg * item_bias[i];
item_bias[i] += learn_rate * bias_update;
}
if (update_j)
{
double bias_update = -one_over_one_plus_ex - BiasReg * item_bias[j];
item_bias[j] += learn_rate * bias_update;
}
// adjust factors
for (int f = 0; f < num_factors; f++)
{
double w_uf = user_factors[u, f];
double h_if = item_factors[i, f];
double h_jf = item_factors[j, f];
if (update_u)
{
double uf_update = (h_if - h_jf) * one_over_one_plus_ex - reg_u * w_uf;
user_factors[u, f] = w_uf + learn_rate_u * uf_update;
}
if (update_i)
{
double if_update = w_uf * one_over_one_plus_ex - reg_i * h_if;
item_factors[i, f] = h_if + learn_rate_i * if_update;
}
if (update_j)
{
double jf_update = -w_uf * one_over_one_plus_ex - reg_j * h_jf;
item_factors[j, f] = h_jf + learn_rate_j * jf_update;
}
}
}
